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We are looking for applicants who have or expect to receive a PhD degree before joining the project. Applications should have a relevant track record in multimodal machine learning, preferably in
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behaviour: (multiple) market places, collectives and networks. Simulations: computer experiments, digital twins, hardware in the loop experiments, computational economics/social sciences, network flows, agent
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) for engineering systems and structures, as well as expertise in machine learning, stochastic modeling, and Bayesian statistics. Programming Skills: Proficiency in programming languages such as Python, C, or R
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strategies (e.g. predictive or machine learning approaches) to improve performance and reduce costs. Collaborating with industrial partners on design optimization, life-cycle analysis, and business case